Supply Chain Security, in plain English.

Hypothesis Quality: Griffin AI vs Mythos

Two AI bug hunters can both generate hypotheses. Only one can defend them. A field study of grounded versus ungrounded hypothesis generation in zero-day discovery.

Securing Claude Code MCP Server Deployments

Claude Code MCP servers run with the privileges of the developer who invoked them. That makes deployment posture the entire security model.

Vulnerability Scanning for AI Models: A New Frontier

AI models ship with dependencies, use vulnerable libraries, and introduce novel attack surfaces. Traditional scanning is not enough.

Griffin AI vs xAI Grok for Security

Per-Scan Token Cost: Griffin AI vs Mythos

Tiered models and a deterministic engine cut token consumption to the moments that need reasoning. Pure-LLM tools pay full price for every trivial check.

Air-Gapped Environments: Griffin AI vs Mythos

Air-gapped AI is not a feature flag. It is an architectural commitment, and it separates serious enterprise products from consumer-grade assistants.

Remediation PR Quality: Griffin AI vs Mythos

Griffin AI produces draft PRs with taint paths, exploit hypotheses, and disproof attempts. Mythos-class pure-LLM tools skip those anchors, and PR quality suffers.

Griffin AI vs Llama 3 for Security Workflows

Llama 3 is a powerful open-weight foundation model, but security workflows demand more than raw inference. Here is how Griffin AI compares.

Enterprise AI Security Rollout: The Governance Gap

Most enterprises rolled out AI-for-security tools faster than their governance processes could keep up. The resulting gap is where most of the pain from 2025 deployments lives.